Regionalising a soil-plant model ensemble to simulate future yields under changing climatic conditions

Models are supportive in depicting complex processes and in predicting their effects. Climate models are applied in many areas to assess the possible consequences of climate change. Even though Global Climate Models (GCM) have now been regionalised to the national level, their resolution of down to 5x5 km2 is still rather coarse from the perspective of a plant modeller. Plant models were developed for the field scale and work spatially explicitly. This requires to make adjustments if they are applied at coarser scales. The regionalisation of plant models is reasonable and advantageous against the background of climate change and policy advice, both gaining in importance. The higher the spatial and temporal heterogeneity of a region, the greater the computational need. The (dis)aggregation of data, frequently available in differing resolutions or quality, is often unavoidable and fraught with high uncertainties. In this dissertation, we regionalised a spatially-explicit crop model ensemble to improve yield projections for winter wheat under a changing climate. This involved upscaling a crop model ensemble consisting of three crop models to the Stuttgart region, which has an area of 3,654 km2. After a thorough parameter estimation performed with a varying number of Agricultural Response Units on a high-performance computing cluster, yield projections up to the year 2100 were computed. The representative concentration pathways of the Intergovernmental Panel on Climate Change (IPCC) RCP2.6 (large reduction of CO2 emissions) and RCP8.5 (worst case scenario) served as a framework for this effort. Under both IPCC scenarios, the model ensemble predicts stable winter wheat yields up to 2100, with a moderate decrease of 5 dt/ha for RCP2.6 and a small increase of 1 dt/ha for RCP8.5. The variability within the model ensemble is particularly high for RCP8.5. Results were obtained without accounting for a potential progress in wheat breeding.Modelle helfen uns dabei, komplexe Prozesse abzubilden um Vorhersagen über deren Wirkung treffen zu können. Klimamodelle werden in vielen Bereichen eingesetzt, um die möglichen Konsequenzen des Klimawandels abzuschätzen. Auch wenn globale Klimamodelle (GCM) inzwischen bis hinunter auf die nationale Ebene regionalisiert wurden, ist ihre Auflösung mit bis zu 5x5 km2 aus der Sicht der Pflanzenmodellierung noch immer recht gering. Da Pflanzenmodelle für die Feldskala entwickelt wurden und deshalb räumlich explizit sind, muss eine Anpassung erfolgen, um sie auf größeren Skalen als der Feldskala anwenden zu können. Die Regionalisierung von Pflanzenmodellen ist nicht nur in Verbindung mit Klimasimulationen sinnvoll, sondern generell in der Politikberatung. Hier wie dort gewinnen regionale Anwendungen an Bedeutung. Je höher die räumliche und zeitliche Heterogenität einer Region, desto größer ist die benötigte Rechenkapazität. Die (Dis-)Aggregierung von Datensätzen, die oftmals in unterschiedlicher Auflösung oder Qualität vorliegen, ist meist nicht zu vermeiden und mit hohen Unsicherheiten behaftet. Das Ziel dieser Dissertation ist die Regionalisierung von räumlich-expliziten Simulationen des Pflanzenwachstums, um Ertragsprojektionen für Winterweizen unter einem sich wandelnden Klima zu erhalten. Dafür wurde ein Pflanzenmodellensemble, bestehend aus drei Pflanzenmodellen, auf die Ebene der Region Stuttgart, mit einer Fläche von 3.654 km2, skaliert. Nach einer sorgfältigen Parameterschätzung basierend auf drei verschiedenen Sets von Landwirtschaftlichen Response Units auf einem High Performance Rechencluster, wurden Ertragsprojektionen bis zum Jahr 2100 berechnet. Die repräsentativen Konzentrationspfade des Intergovernmental Panel on Climate Change (IPCC) RCP2.6 (drastische Reduzierung der CO2-Emissionen) und RCP8.5 (Worst-Case-Szenario) dienten als Rahmen für die Simulationen. Das Modellensemble zeigt im Ergebnis stabile Winterweizen-Erträge bis 2100 für beide Szenarien, mit einem Rückgang von 5 dt/ha bei RCP2.6 und einem geringen Anstieg von 1 dt/ha bei RCP8.5. Insbesondere bei RCP8.5 ist die Variabilität innerhalb des Ensembles sehr hoch. Zu berücksichtigen ist, dass der Züchtungsfortschritt in den Ergebnissen nicht abgebildet wurde

AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat, SDATA-20-01059

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033).Two scientific publications have been published based on some of these data here

Multi-wheat-model ensemble responses to interannual climate variability

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts

A framework to introduce flexibility in crop modelling: from conceptual modelling to software engineering and back

Keywords: model structure, uncertainty, modularity, software design patterns, good modelling practices, crop growth and development. This thesis is an account of the development and use of a framework to introduce flexibility in crop modelling. The construction of such a framework is supported by two main beams: the implementation and the modelling beam. Since the beginning of the 1990s, the implementation beam has gained increasing attention in the crop modelling field, notably with the development of APSIM (Agricultural Production Systems sIMulator) in Australia, OMS (Object Modelling System) in the United States, and APES (Agricultural Production and Externalities Simulator) in Europe. The main focus of this thesis is on the modelling beam and how to combine it with the implementation beam. I first explain how flexibility is adopted in crop modelling and what is required for the implementation beam of the framework, namely libraries of modules representing the basic crop growth and development processes and of crop models (i.e. modelling solutions). Then, I define how to deal with this flexibility (i.e. modelling beam) and more specifically I describe systematic approaches to facilitate the selection of the appropriate model structure (i.e. a combination of modules) for a specific simulation objective. While developing the framework, I stress the need for better documentation of the underlying assumptions of the modules and of the criteria applied in the selection of these modules for a particular simulation objective. Such documentation should help to point out the sources of uncertainties associated with the development of crop models and to reinforce the role of the crop modeller as an intermediary between the software engineer, coding the modules, and the end users, using the model for a specific objective. Finally, I draw conclusions for the prospects of such a framework in the crop modelling field. I see its main contribution to (i) a better understanding in crop physiology through easier testing of alternatives hypotheses, and (ii) integrated studies by facilitating model reuse. </p

Plant Modelling Framework: software for building and running crop models on the APSIM platform

The Plant Modelling Framework (PMF) is a software framework for creating models that represent the plant components of farm system models in the agricultural production system simulator (APSIM). It is the next step in the evolution of generic crop templates for APSIM, building on software and science lessons from past versions and capitalising on new software approaches. The PMF contains a top-level Plant class that provides an interface with the APSIM model environment and controls the other classes in the plant model. Other classes include mid-level Organ, Phenology, Structure and Arbitrator classes that represent specific elements or processes of the crop and sub-classes that the mid-level classes use to represent repeated data structures. It also contains low-level Function classes which represent generic mathematical, logical, procedural or reference code and provide values to the processes carried out by mid-level classes. A plant configuration file specifies which mid-level and Function classes are to be included and how they are to be arranged and parameterised to represent a particular crop model. The PMF has an integrated design environment to allow plant models to be created visually. The aims of the PMF are to maximise code reuse and allow flexibility in the structure of models. Four examples are included to demonstrate the flexibility of application of the PMF; 1. Slurp, a simple model of the water use of a static crop, 2. Oat, an annual grain crop model with detailed growth, development and resource use processes, 3. Lucerne, perennial forage model with detailed growth, development and resource use processes, 4. Wheat, another detailed annual crop model constructed using an alternative set of organ and process classes. These examples show the PMF can be used to develop models of different complexities and allows flexibility in the approach for implementing crop physiology concepts into model set up

A process-based crop growth model for assessing Global Change effects on biomass production and water demand - A component of the integrative Global Change decision support system DANUBIA -

Spatial and temporal changes in crop water demand are of fundamental significance when examining potential impacts of Global Change on water resources on the regional scale. Carried out within the project GLOWA-Danube, this study investigates the response of crops to changing environmental conditions as well as to agricultural management. As a component of the integrative Global Change decision support system DANUBIA, a process-based crop growth model was developed by combining the models GECROS and CERES. The object-oriented, generic model comprises sugar beet, spring barley, maize, winter wheat and potato. The modelled processes are valid for all crops and mainly comprise phenological development, photosynthesis, transpiration, respiration, nitrogen demand, root growth, soil layer-specific water and nitrogen uptake, allocation of carbon and nitrogen as well as leaf area development and senescence. Attention is given to crop-specific differences through assignment to crop categories (e.g. C4 photosynthesis type) and a set of crop-specific parameters. The model was validated by comparing simulated data with several sets of field measurements, covering a wide range of meteorological and pedological conditions in Germany. Furthermore, the responsiveness of the model to Global Change effects was examined in terms of increased air temperatures and atmospheric carbon dioxide concentrations. The results show that the model efficiently simulates crop development and growth and adequately responds to Global Change effects. The crop growth model is therefore a suitable tool for numerically assessing the consequences of Global Change on biomass production and water demand, taking into account the complex interplay of water, carbon and nitrogen fluxes in agro-ecosystems. Within DANUBIA, the model will contribute to the development of effective strategies for a sustainable management of water resources in the Upper Danube Basin

Crop models and their use in assessing crop production and food security: A review

Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security

Modelling the soil water balance of potatoes for improved irrigation management

Soil Water Balance (SWB), is a generic and mechanistic crop growth model that has been successfully used to model the water balance of several crops. Its ability to combine crop water modelling and irrigation scheduling approaches allows it to be used as a research tool and an irrigation management tool. Since SWB is a tool that could be used as decision making tool for farmers, its accuracy in simulating crop growth, development and soil water balance should be high. To highlight the importance of improved irrigation management for potato crop by the means of a mechanistic soil water balance model and the importance of the photoperiod factor in potato modelling in sub-tropical region, two potato experiments were carried out in two contrasting seasons, namely, spring and autumn. Growth and development responses of potato under both well irrigated and water stressed conditions for spring and autumn plantings were examined. This study successfully quantified the water use and potato growth responses to water stress. The water use efficiency varied with irrigation treatments and planting time, and autumn experiment had generally higher values than spring. Unstressed treatment gave the highest tuber yields irrespective of planting season and marketable tuber yield was higher in autumn than spring. Water stress imposed at tuber initiation until end of tuber bulking was revealed to be the most detrimental to biomass and tuber production. This suggests that water stress at tuber initiation and bulking stage should be avoided if high tuber yield is the target. Growth analysis data were used to determine crop parameters for SWB calibration and validation. The model simulated reasonably well growth, development and soil water balance in both unstressed and stressed conditions. However, simulations results of total and harvestable dry matter towards the end of the exponential tuber bulking stage (50 - 65 DAP) were deteriorated. As a result, the model did not simulate accurately the final yield. This is an indication that the model fails to simulate the size of the canopy and its duration. The time at which tuber initiation commences appeared not be affected by the planting seasons since variation of the duration between emergence and tuber initiation in different seasons was small. This small variation could be attributed to the fact that the potato growing season in South Africa (Pretoria) in spring 2004 and autumn 2005 experiences minimum and maximum temperatures which are acceptable for the growth of potato. In Pretoria, emergence and tuberisation take place under relatively cool temperatures late in September and also early in April when temperatures are relatively cool. Consequently, potato grown in this period may escape the early autumn and late spring high temperatures. However, autumn planting experiences an abrupt change of day lengths from long days to short days towards tuber initiation. This brusque change of day length may change the crop physiology and affect the subsequent normal course of plant growth. If the day length factor could be integrated into SWB, it appears that the model will better simulate potato growth and development. The poor simulation results of total dry matter and harvestable dry matter early in the growing season suggest that the model should be improved by allowing it to simulate the start of tuber initiation. A linear function of average temperature between a base and an optimal temperature corrected with photoperiod factor was found to be the most appropriate method to estimate thermal time required for tuber initiation. This method suggests that the time of tuber initiation can be estimated from its thermal time within two days.Dissertation (MSc (Soil Science))--University of Pretoria, 2007.Plant Production and Soil ScienceMScunrestricte

Adaptation d'un modèle de culture et conception d'un modèle de décision pour la gestion conjointe de l'irrigation et de la fertilisation azotée du blé dur

Les changements de contexte économique, réglementaire et environnementaux auxquels la production agricole doit faire face nécessitent d'évaluer de nouvelles stratégies de gestion conjointe de l'irrigation et de la fertilisation pour blé dur. Le travail de thèse a suivi un double objectif : i) adapter le modèle de culture STICS à différents cultivars de blé dur, et ii) concevoir un modèle de décision des pratiques de fertilisation azotée et d'irrigation. Une base de données comportant 373 traitements expérimentaux issus de douze années climatiques a été constituée à partir d'expérimentations réalisées avant la thèse à l'INRA et ARVALIS puis mobilisée pour conduire le travail d'adaptation et d'évaluation du modèle de culture. L'adaptation du modèle de culture a consisté dans un premier temps en un travail d'estimation de paramètres par optimisation mathématique pour sept cultivars de blé dur. Une analyse comparative de trois démarches a permis de sélectionner la démarche pertinente pour obtenir un modèle précis et robuste pour la simulation du rendement, de la teneur en azote des grains et des variables intermédiaires (Biomasse aérienne, surface foliaire, quantité d'azote absorbée) dans différents contextes pédo-climatiques (large gamme de niveaux de nutrition hydrique et azotée). Cette étude fournit un cadre méthodologique pour l'estimation des paramètres des modèles de culture. Les résultats de ce travail ont de plus démontré que le modèle de culture, avec son formalisme initial, n'était pas sensible à l'effet du fractionnement de la fertilisation sur la quantité d'azote et la teneur en azote des grains. L'adaptation est appréhendée dans un deuxième temps par la modification du formalisme d'accumulation de l'azote dans les grains par l'introduction d'un formalisme inspiré du modèle AZODYN. La modification n'a pas amélioré suffisamment la sensibilité du modèle à l'effet du fractionnement sur la teneur en azote des grains. Le manque de données expérimentales en phase post-floraison et notamment la dynamique de la sénescence foliaire n'a pas permis d'améliorer la capacité du modèle de culture à simuler les processus d'absorption d'azote du sol après la floraison. A partir d'une enquête auprès de 29 irrigants de blé dur, les pratiques et les stratégies de fertilisation azotée et d'irrigation, ainsi que les décisions stratégiques et tactiques ont été identifiées et formalisées dans un modèle de décision. Une évaluation de stratégies conçues sur la base des résultats d'enquêtes est proposée comme illustration de l'utilisation du modèle STICS adapté au blé dur et du modèle de décision formalisé. Le couplage informatique du modèle de culture STICS au modèle de décision permettra de disposer d'un modèle bio-décisionnel et ainsi pourra être utilisé pour concevoir et évaluer des stratégies de gestion conjointe de l'irrigation et de la fertilisation azotée du blé dur adaptées au contexte des exploitations agricoles. ABSTRACT : Changes in economic, regulatory and environmental context of agricultural production raise the need for research to evaluate and propose new strategies for joint management of irrigation and fertilization for durum wheat. The thesis had two objectives: i) adapting the simulation crop model STICS to different durum wheat cultivars, and ii) designing a decision model for nitrogen fertilization and irrigation practices. A database containing 373 experimental treatments carried out by INRA and ARVALIS before this PhD work was established and mobilized to conduct the adaptation and the evaluation of crop model. The adaptation of the crop model was first conducted through durum wheat parameter estimation by mathematical optimization. A comparative analysis of three approaches was conducted to select an appropriate approach to obtain an accurate and robust crop model for the simulation of grain yield, grain nitrogen content and intermediate variables (biomass, leaf area, amount of nitrogen absorbed) in different soil and climatic conditions. This study provided a methodological framework for crop models parameters estimation. The results of this study showed that the crop model, with its original formalism, was not sensitive to the effect of splitting of fertilization on the grain nitrogen content and protein concentration. The adaptation was then conducted through the modification of the formalism of nitrogen accumulation in grains by introducing a formalism inspired the AZODYN crop model. The modification did not significantly improve the model's sensitivity to the effect of N splitting on the nitrogen content of grain. The results of this study call into question the ability of crop model to simulate the absorption process of nitrogen after flowering. Unfortunately the lack of data concerning post-flowering leaf area dynmaics did not allow improving the model. From a survey of 29 irrigators, practices and strategies of nitrogen fertilization and irrigation, as well as strategic and tactical decisions have been identified and formalized in a decision model. An evaluation of strategies based on survey results is given as an illustration of the potential use of the STICS soil-crop model and the decision rules identified and formalised. The coupling of the crop model to the model decision will allow proposing and evaluating strategies adapted to the farm context for joint management of irrigation and nitrogen fertilization of durum wheat